Salesforce AI Day: Calling All Trailblazers

We brought you into the cloud. Software is totally superfluous, led you through mobile, through social. We pioneered predictive AI for CRM. Now we enter a new era together where we unlock experiences that were once.

The stuff of dreams.

While AI has forever changed our world, we need to ask more of AI and build trust into every experience.

Making.

Sure no one gets left behind. All of this so we can connect with customers in a whole new way. Say hello to the new sales force, Reimagined for an AI first world with trust at its core. Calling all trailblazers, join us because business.

Will never be the same.

Salesforce Chairman and CEO Marc Benioff.

Thank you. All right, all right, all right. Well, good afternoon, everybody. How are you doing? Great. Fantastic. Isn't this a beautiful room? We've done so many incredible events in this room over two decades. We're so happy to be back here to talk to you about artificial intelligence, one of the most important technologies of our lifetime. Well, I think artificial intelligence and Generative AI, well, maybe it's the most important technology of any lifetime. And we're going to talk about that today. What's happening with it? What's happening with all of you? And we're so excited to have all of you. We're going to do our best to educate you, to inspire you and to motivate you and even entertain you. But before we can do any of those things, do you know what we have to do? We have to thank you. We have to thank each and every one of you for everything that you do for us every day. We're so grateful for what you do for us. And thank you very much. And thank you for being here today. Thank you for your great support of our company. Thank you for your continued relationship with us, and we're so grateful to each and every one of you. I want to also thank one of our customers who's inspired me so very deeply in Generative AI, and I thought it would be appropriate to have them here. So, Vasily, will you just stand up and be. Please welcome our friend from Italy. Vasily, who's here from Gucci, is with Cloud 9. And I think a lot of folks don't know too much about Gucci9 and what you're doing. And I just want to thank you so much for inspiring us. I would say you're kind of customer case number zero for us for Generative AI. And anyway, just give us some inspiration. You've come so far to be with here and tell us what you've been doing. Mark 1, 2, Mark, thank you so much for the huge sponsorship and support that we have and the amazing partnership. Thank you to your team. Thank you. Silvio from the science department. Clara. We started this journey in uncharted waters one year and a half ago, trying to see how with AI we can augment the capabilities of our advisors. Because in Gucci, our mantra is the human touch powered by technology. So we started testing how we can Guccify the tone of voice and Guccify Gucci fy. Isn't it obvious? That suit is very Gucci. We should do something for September here. Help me and very importantly, Gucci me for September for Dreamforce. I need to be Gucci. Florence should be working tomorrow on this. Thank you. And very importantly, how we can test it. Gucci9 are our client services that we have across the world. Powered by Salesforce, where we brought beauty in our client services. The aesthetics of our stores are there. Our advisors create unique relationships, craft relationship with our clients. So we tested how AI can augment this. We measured it, we saw positive results and the journey just starts. We are the business case zero in AI, at least in the luxury arena, I hope. Thank you so much, Mark. Well, thank you so much. And I want to thank you so much. And I'll tell you, while you're standing, I want to just ask my whole AI research team, we have a few members here to stand up with you or with our AI researchers or engineering team. We just stand up. We have Silvio and Simon over here. Thank you, Silvio. Steven's over here. Very good. Thank you. Been working very closely with Gucci. Thank you. Okay, well, we're so happy to been doing this incredible work and for a year and a half, we've been using our generative AI with Gucci. And what we saw this incredible thing happen. And what happened was is we're working with the call center and the customer service group and it's very powerful. And we saw that the service agents can do so much more. They become augmented. They begin to have more capabilities where they were into service before. Now they can have marketing, now they can have sales. Now they can have commerce. They have more capabilities. And I think maybe this is one of the great promises of generative AI to augment our capabilities. So I want to thank you so much for giving us that vision of what the possibilities are and the tremendous work that our AI team has done with you. Bringing our own LLMs, bringing third party LLMs, open source LLMs and finding this incredible combination using Salesforce's apps, but also your data and your Incredible employees to achieve this result. And I guess if we could take everyone not just here in this room, but also everyone online and to do the same thing that's happened with Gucci, it would be a tremendous, tremendous accomplishment for productivity and also for the promise of augmented and generative capabilities so that we start to get the next generation of artificial intelligence. Please thank our good friend here. Thank you so much. We have so many exciting things to talk about, and we have a great group of speakers here, and I'd like to invite them up. We have Clara Shih, who's our CEO of Salesforce AI. So, Clara, would you come up here and be with us up here on our panel? Okay. Coming from the curtain, Patrick Stokes, the EVP of product and Industries Marketing. Kathy Baxter, our principal architect of ethical AI. And the most probably important thing we're doing with AI is trusted and responsible. AI Srini, our president and chief engineering officer, works directly for me. Thank you. Srini has done an incredible job making all of this happen. And also Julie Sweet, who is the chief executive officer of Accenture, is also joining us today. So please welcome all of them. Would you? Well, Salesforce has been on an incredible journey, and these leaders have been bringing us forward. And of course, all of our 70,000 Salesforce employees all over the world are doing so much to take all of our customer relationships forward, especially with this incredible new technology that you're all going to see. And probably it's probably best represented by this amazing slide right here where you can really see the tremendous growth of Salesforce now over 24 years. And none of that would be possible without my co founder, Parker Harris, who's standing right here. I just had breakfast with stand up Parker, Would you? And I think that when Parker and I started this company 24 years ago, we never anticipated that we'd get here today quite like this. And to see this growth, but also to see this incredible quarter we just had with these incredible numbers, but also to see how the company has received so many other accolades. So thank you, Parker, for everything that you're doing. Thank you, Mike. Really appreciate it. Probably the best thing that Parker and I did, which was we put 1% of our equity profit in time into a 501c3 nonprofit 24 years ago. It was very easy at the time because there was no equity, profit or employees or anything. It's a joke that still works, which is kind of amazing, but it really paid out as business is the greatest platform for change. We've been able to give back more than $621 million in grants to local nonprofits and NGOs. Thank you. 8.1 million hours of volunteerism has been accomplished by our employees and 54,000 nonprofits and NGOs running for free on our service. Probably the greatest thing that we've achieved is not just doing it ourself, but we've inspired 17,000 other companies to join us in our 111 model, and that is incredible for us. So I want to thank all of them. And I think we have a few nonprofits and NGOs here, including Maya and Amber from Marcy and Robin Hood Foundation. And if you're with a nonprofit or ngo, would you just stand up and be recognized? Because we love to do that in our program. So right over here, Here, here. Very good. Thank you. Thank you for everything that you're doing for us. Every day at Salesforce, it always comes back to our core values. How are we operationalizing those values into our company, into our community? That's about trust. It's about customer success. It's about innovation. It's about equality. It's about sustainability. Today, we're going to talk a lot about trust, which is a critical part of what we're going to do with generative AI. And when we talk about the augmentation of the human beings to give them even more capability or more productivity, it's going to have to be done with a lot of trust. We're going to talk a lot about innovation, which is why I'm so happy that Srini is here with his team and why we're here with so many of our engineers to really show you what we're doing and what we're about to release over the next few days to all of our customers. You're gonna see the beginning of our June release and our July release, which are filled with these technologies. And as we get to Salesforce Dreamforce conference in September, I believe that you're gonna see our entire product line transform completely, change to advance, to get to another level. And it's going to be extremely exciting to see this capability happen so fast. Now, probably one of the fastest things that we've seen really happen is the growth of this incredible new generative AI. But what I'd like to show you today is this idea of where our AI capabilities have gone. And for those of you who have been with us since 2014, you saw us first launch Einstein. That really came out of where we all had kind of an existential freak out at Salesforce around 2010. That's when we started to think this AI revolution was gonna happen and we had to get going. We started to acquire a lot of amazing companies and bring a lot of incredible minds into the company. And we built this amazing platform called Einstein. And that was very much the beginning of our AI journey. Today is another critical step on it, but I'd really like Srini to kind of walk us through what we've been doing since we first introduced einsteini thank you.

Okay, this is good. So I think Einstein is AI for CRM. We've been pioneering AI for CRM since 2014 and today Einstein does about a trillion predictions every week. So what does that mean, trillion predictions? If you are a sales cloud customer, if you use our lead scoring app, Einstein lead scoring, you can convert your leads 3x faster just in Thanksgiving week. If you're a commerce cloud customers, we generate more than 50 billion AI product recommendations and customers see a 300 million GMV uplift. Now to do this, we had to first invest a lot in hiring world class talent. We acquired some great companies like MetaMind, RelateIQ, PredictionIO, and coupled it with a lot of organic hiring. So a world class AI team, both researchers, data scientists, data engineers and all invested a lot. That's one thing we had to do. We also had to solve a lot of fundamental problems because one of the things that you want to do in AI, especially in the predictive case, was as you're doing machine learning, we cannot see the data. All the data scientists want to see a lot of data and we hire all of them. They say, srini, give me access to all your data. He said, no, you cannot. So what we had to do is we had to invent new techniques like auto feature engineering, auto feature selection, auto model selection, and we open source a lot of these. We had to invent new technologies. Some of the other things as we learned as we were doing it, where as we invested with deeply invested with our customers, new use cases started coming. This is when we started investing in LLMs and, and some of the early papers that we have invested, like deca, NLP and all, were suggesting how prompting could be a new technology that we could invent. And then what we did is we said, if this is where the world is going, let's invest deeply in this LLM technologies. And we learned very interestingly that some of these LLMs are autoregressive techniques. And then we said we could really interestingly we could use it for protein generations. And then we published a lot of protein generation papers in Nature magazine and all. So we Started seeing this early one. So what we want to do in the predictive AI, what we did was we brought all these technologies for our customers. So we absorb abstract all of the technology so that you can get business wins. What we are going to do is in the new world of generative AI, we still have to solve the trust problems, security problems, scale problems, privacy ethics. We'll solve all of that and bring you. And that's what we are excited about, to bring you to the future. And that's what today is about. Thank you, Mark.

Very good. All right, thank you so much, Srini. I want to just stay on this slide for one minute and I'll tell you, I don't think a customer interaction or story or meeting today starts or ends without this idea of what's really happening with generative AI. And the story usually goes like this. I know about these new large language models. Many of these models are amazing. A lot of us have used ChatGPT and this kind of GPT4 model. Also a lot of the models that have been created by our own engineering teams are amazing. And many of the startups who are in this room are also building these amazing models. And so these CEOs are so inspired or CIOs that we're talking to and they're like, you know, what we're going to do is we're going to start working with these models and we're going to take all of our corporate data, we're going to put it into the LLM and then all of a sudden we're going to have instant intelligent company. And it's not quite like that. Okay? It's not quite like that. Now the reason why they think that is they already know how these other more public models are working. They take these huge vacuum cleaners and they're just vacuuming up all of the data off the Internet that they can get. Okay? And you see that. So they're all just taking, if there's publicly available data or just data that's out there off of an Internet that can be scraped, they're taking it and training their models, which as much data as they can bring down. And then they're taking that data and then they're turning on their LLM and whatever comes out of it is great. And if there is this concept of hallucination or if the LLM basically starts to lie, well, that's not really their responsibility. Their responsibility is to kind of give you the best case they can with their generative artificial intelligence. That's not exactly the world of trust that we live in. And recently I was with one of our largest customers right here in New York, very large bank. And the CEO made it very clear that they want to use LLMs to become much more productive in mortgages and account service and all the capabilities that a bank will do. So can they do that? Can they just take all of their account information and all of the history of all the accounts and everything and just put it into an LLM? Well, I don't think that's going to work out very well when it comes to kind of the regulated industries and the way that data works in large companies. That's why really the onus becomes on us to give our customers this next generation of generative AI. And that's what you're gonna see today. Because as I mentioned earlier, and this is gonna ground our presentation today, trust is our number one value at Salesforce. We saw that when Parker and I first designed the product, which was way back in 1999, we came up with something called the sharing model. And that idea was that every person in the company who gets Salesforce access, they also get access around what data they can see or not see or use. Because we know especially like for a bank, every bank executive cannot see what every other bank executive can see. Or in healthcare, what one healthcare executive can see, another healthcare executive can see. We all understand the enterprise sharing model, don't we? We all understand what that means in a company. And we also understand that a lot of us in enterprise, it come from the relational model. And the relational model is kind of the rows and columns model of data where its security is down to the cell level. Not only were readers don't block writers, but the idea that cells can be locked by one particular user so that cell cannot be seen by another user, that is not the kind of generative AI that we're all experiencing, is it? Because the way these large language models work is they're taking down all that data that they grab and then they're amalgamating and tokenizing that data and then using their algorithms then to generate their intelligence. So that idea of cell based security is not in the current generative model. That's the breakthrough that we're really gonna try to show you today, where we really are leveraging, starting back in 2016, where we came up with our first trust model for predictive. Now, Parker and I were joking about this today because I always called it anonymous predictions. Because the idea is that when our systems, when our applications, when our platform looks at all of your data, and then uses machine intelligence or machine learning or deep learning, which are kind of the three primary artificial intelligence techniques that really existed before the current generative AI techniques. We don't look at your data. We're able to provide you those predictions and that AI capability without actually looking inside the data by just keeping it anonymous. And now with generative AI, what we're able to do is we're able to take the same technology and the same idea to create what we call a GPT trust layer, which you're going to see today for the first time. And we're about to roll out to all of our customers so they have the ability to use generative AI without sacrificing their data privacy and data security. This is critical for each and every one of our customers all over the world. Every transaction and every conversation at Salesforce begins and ends with the word trust. So we understand that very well. And there's one other critical part of all of this. It's not just about trusted AI and delivering the technology to the right person at the right time, but it's also about responsibility. As we're all going to learn. Because we're now on a societal AI journey, there is going to be a lot about responsibility in this technology. We've all seen the movies and we've all seen where this can go, haven't we? We all have these crazy ideas in our head of what could happen. There's many different possible scenarios. So that's why responsible AI use is so critical and why I'm so excited that we have an incredible AI ethics team that has been placed at Salesforce for many years. And Kathy Baxter is here, who wrote an incredible article that got published in HBR this week on ethics and AI. But I asked her to speak on our panel. And Kathy, would you just ground us right now in responsibility of AI before we begin our technical journey today?

Thank you so much.

Thank you.

In 2019, we published our trusted AI principles. And the very first principle is responsible. We believe that at the core, our responsibility is to ensure that we are protecting human rights as well as the data that all of you, our customers, entrust us with. And this first line, your data is not our product. That is a key differentiator. Your data is not our product. At the beginning of this year, as we began delving even more into generative AI, we recognized that we needed more specific guidelines than the principles that have been driving all of our work. And so we created these five guidelines that are specific to. To not just Generative AI. But generative AI in an enterprise environment, first and foremost, it has to be accurate. You're making business decisions based on the content that's being generated, so it must be accurate. It also has to be safe. We need to assess it for bias and toxicity. It needs to be transparent. We are ensuring that the data provenance of all of the data that trains our models, it's full, fully consented and we are transparent. Whenever content is AI generated, we believe that AI needs to be empowering. So keeping humans in control, giving them the tools that they need to know, is this content useful, is it accurate? And then finally, sustainability is one of our core values. And so when we are building models, we are going to ensure that they are right sized. It's not about creating the best biggest model possible. It's creating the right size model that is going to be most accurate for our customers, but also takes into consideration the carbon and water footprint necessary to train them. Thank you all very much.

Very good. Thank you very much. We're so lucky to have Kathy and also our incredible ethics team and guiding us, I think is going to be a huge burden for them as we start to move forward very rapidly in this new AI journey. We already understand this is going to be a critical part of every single thing that we do in the world that we live in and in these industries that we participate in, like banking or health care or media or so many of the industries that are here in this room with us and watching us all over the world, they like to know exactly where their data is and they want to know where that is and audit that place. And that is not how generative AI works. Generative AI works with this kind of expansion from the deep learning principles, where deep learning kind of had these amazing neural networks and you all of a sudden had these amazing insights and now the networks just got expanded and expanded. And as the network expanded, all of a sudden the ability of the models expanded as well. But as those expansions happen, the weights and how they're moving all are shifting and changing. And that is really the burden of our AI team over here. They have to really be able to use these next generation models, but have that capability to deliver a trusted experience to our customer. We're already the number one AI CRM. We already know that all of our customers are doing a trillion transactions a week using Einstein. There's no company that's even close to what we're doing in the customer relationship management area with artificial intelligence. We understand the burden there for must be on us as we're going to take this forward. We're already trying to do all these incredible things to maximize return on investment for our customers, to deliver them the fastest time to value, to innovate the fastest with low code or no code capabilities. But now giving them the trusted productivity that they're demanding with generative artificial intelligence, how will they augment the employees productivity? Just as we heard from our friends at Gucci, now our taste of that has really started to accelerate earlier last year when we introduced our customers to this incredible new product that we have called data cloud. And data cloud has become our fastest growing cloud ever. And one of the reasons why this is becoming such an important cloud for our customers is as every customer is preparing for Generative AI, they must get their data together. They must organize and prepare their data. So creating a data cloud is so important. But the problem for a lot of our customers is that they might be creating data clouds but with teams or with technologies that are outside of the Salesforce ecosystem. And that's why we extended our Salesforce core platform with this product that was intelligent, real time, automated and hyperscale. We introduced it at Dreamforce last year. It's taken off like a rocket ship and we've seen this incredible capability now. We're already delivering 30 trillion transactions per month with data cloud. And we're importing I think about 12 trillion components of data already on an annual, fully annualized basis. It's incredible what our customers are doing. I mean, one of the amazing stories that we have, we work so closely now with Ford and these next generation component cars that they're building and trucks. I have one of these new lightnings, they're amazing. It's just dripping telemetry and it drips data because of all the technology and all the capability. But it needs a receptacle for that data. It needs the ability to have all the data. So then Ford can provide the next generation sales experience. I just bought a second truck actually because it was so amazing. A service experience, a marketing experience, all of those things are then generated out of the data cloud. So the data cloud sets the stage and the beginning for every customer's AI journey. And then our primary vision, the vision that Parker and I set up with 24 years ago, we're ready then to begin connecting with our customers in a whole new way. This idea that we want to then provide that Customer360 experience, from sales to service to marketing, to commerce, to tableau to slack, to all the capabilities of Salesforce on the 360, but then augmented by generative AI. We already know generative AI is changing everything. We already can see the huge growth. How many people here have already used ChatGPT? Raise your hands, everybody. So I didn't have to call for hands, did I? It's the fastest growing consumer product of all time. We all know the story and it's an amazing experience. But. But we've all seen the limits as well. And we all know that all the data that we're putting in there, as we sit there at home and play things or whatever, well, it's getting stored and you're training that model and that's how it works. And if you wanna understand that, talk to some of the AI experts in the room today and they'll explain to you why that consumer model is so powerful for OpenAI. Of course. But does that work in our enterprise? Does that give us that trusted experience in our enterprise? Is it going to give us trusted productivity? We want the augmentation, we want that next generation capability for our enterprise. But what about the trust? How are we going to store that data when what the LLMs desire is to take as much of that data and then put it into its weights as needed. But is that what we're going to allow it to do with all of our enterprise data? Will we be able to preserve our sharing model? Will we be able to preserve our security model, our privacy model? Will we be able to lock down for each and every customer and each and every employee what they need? That's why we already know that every CEO needs an AI strategy. Now we've been talking about that for over a decade and we have the best team when it comes to enterprise AI, there's no question. But when we look at these new models that everyone's going to roll out, we know one thing and that is there's a pretty big gap. And it takes place in every conversation I have with every CEO. It's this AI trust gap. When we talk about privacy, when we talk about hallucinations, when we talk about data control, when we talk about bias, when we talk about toxicity, these are technical terms actually in AI, they're not like kind of societal terms. These are actual technical explanations of things that are happening inside these models. That we realize that over here we have this desire to rapidly move forward to have more productivity, but over here we have this need for trust. And what we hope to do today is to really close that gap. This is a critical time. That's why I mentioned we already have delivered that first generation of our trust layer with our productive model. In 2016, when we rolled out Einstein, we're ready to cross that bridge. And every customer who's in this room and online who uses Einstein has already crossed that bridge. We never had to say to anyone, oh, yes, we had to look at all your data to make a prediction. No, we didn't have to do that. And we also know that we have to apply it to all of our applications, not just some of them. And we also need to then say, we're going to take all of this to not only be number one AICRM in predictions, but number one AICRM when it comes to this incredible generative AI capability. So that is why we're introducing today our AI cloud, trusted enterprise AI, built for CRM, built for Salesforce, built for our customers, implementing these key technologies that are so critical for us using our Einstein GPT trust layer, which is about to get rolled out for all of our customers worldwide, allowing all of our applications to deploy that capability and augmenting our own capabilities to be the number one AI CRM to help us understand exactly how this works. I would like to introduce you to Patrick Stokes. And Patrick, welcome. Please give him a hand.

Thank you very much. All right, thanks, Mark. Thanks, everybody. So we're going to answer and bring to life the most important question of the day, which is, how do I trust Generative AI? Maybe more importantly, how do I leverage all of the productivity gains that we can get from Generative AI without giving away all of our company's data? Now, Mark talked a little bit about this. We kind of understand how it works today, right? We put our data into databases, and databases have this kind of inherent concept of location. You put the data into a database, you're specifying what database it goes to, what table, what row, what field, et cetera. And within that location, we can put access controls on top. We can specify who's allowed to pull the data out of that particular location. And on top of that, we can build all of the security, all of the data governance, all of the access controls that we need to keep our data safe across all of the employees that we have and define how that data ultimately gets used. But large language models are completely different because they don't really store data. Instead, they learn data. And learn data is very, very different. If I asked you all in the room what an apple is, you can probably immediately tell me what an apple is. But if I asked you to tell me where in your brain is the knowledge about what an apple is, nobody would be able to tell me that a large language model works very much the same way. The reason you know what an apple is is because over time you've come to identify certain properties about the apple. You know that an apple is round, you know that it grows on trees, you know that it's red, but sometimes it's green. And all of these properties combined give you the knowledge of what an apple is. But in that stored environment, excuse me, in that learned environment, we can't put those same types of access controls on top. We can't control how the data comes out of the large language model, how the recall comes out. So that's the problem that we're trying to solve.

Now.

How do we solve it? Well, it all starts with a prompt. Now, prompt is a word you're going to hear a whole hell of a lot over the next couple years and months. A prompt is, you've all done this probably if you've used ChatGPT. A prompt is just a question. It's the question that you're going to ask the large language model. So let's consider things in the context of a business here. Let's pretend I'm an investment manager at a bank and I want to write an email inviting my client to discuss our investment services. Fairly simple question. And from this fairly simple question, I am going to get an answer or a generation. That is two things at the same time. First, it's amazing, it's incredible what this was able to generate with very, very little information. This is something that I can take, I can edit, I can manipulate a little bit, and this is an amazing first start. But this is also terrible and unusable, right? It's not personalized. It doesn't know anything about my customer or my company or the products that I sell. This reminds me of every recruiting email I get on LinkedIn. Right? It's just right to the trash. I can't really use this and it's because it's missing context, it's missing information about my business. Now I want to include that information. Now what most of you would probably think is, ah, I know what I need to do. I need to train a large language model. I need to spend months of time giving it data and feeding all of this data about my business into the large language model to train it. But you don't need to do that. You can continue to use the prompt in a technique called grounding. Because a prompt is more than just a question. A prompt is an entire canvas to provide detail, context and instructions. And within those instructions, we can ask for more of what we want. So for example, within this prompt, I can tell them about myself. I'm Patrick Stokes, I'm an investment manager at Cumulus Bank. I can tell the prompt about my customer or my company. Excuse me. We're a one stop solution for personal banking, loans and investments. I can tell the prompt about my customer, Lauren Bailey. She's been a customer for over seven years. She's gold tier status. She has a checking account, savings account, et cetera. I can even tell this prompt about some of the most recent information. Have you all heard that? Large language models, most of them today in the consumer space, they're like a year in fact, maybe more out of date. That's not good enough. We need to know exactly what's going on in our business right now, so we can put that in the prompt. Lauren downloaded an empowering sustainable future white paper from our website last week. So we know she's interested in sustainable investments or a sustainable future. I can include information about our newest products that we've just launched, we've actually just launched a new green energy investment opportunity. And then finally I can include information that may be true, may not be true at any given time, which is we're having an event. So maybe we're having an event in a few months, maybe we're not in a few weeks. And this is the type of kind of logic instruction that we can include as well. And when we ask for a generation based on all of that, we get something that is immediately usable. This isn't something that we have to download and cut and paste and start injecting our data into and then worry about where we paste it back. This is something I can just click a button and use immediately in my application. But there's still a problem. The problem is that all of that customer data is put in there. Now, before I explain how we're going to solve that problem, I want you to just consider all of the data that Salesforce has about your business. All of the context across sales service, commerce, marketing across the data cloud, all of that telemetry data coming from Mark's car, his two cars, all of that data coming in, this is all data that we can ground in that prompt that we can add as context to get a better generation on the other side. But the problem is if you look at that data, that's sensitive data, there's PII data in there, there's Lauren Bailey in there. How do we protect all of that? So we're prompting it, it's better than training. But how do we protect all of that data from getting lost in the large language model and not being able to control control how we recall it later. And that's where the Einstein GPT trust layer comes in. The Einstein GPT trust layer creates separation between all of your corporate enterprise data stored in your CRM in databases where we can apply access controls. And it allows you to responsibly ground all of your prompts in that data without that data ever leaving salesforce. Now we do this with a number of methods. Secure data retrieval, dynamic grounding, data masking. We do toxicity detection and something called zero retention. Now let's take a look at just a few of those. So if we go back to our prompt, let's look at data masking. So as I mentioned, I have some PII data in here. I have Lauren Bailey. That's personally identifiable information. That's information that I don't want to go out into the prompt. So we have a technique called data masking. We can simply mask that data. And now that PII data does not travel across on the prompt with it. But even cooler than that is we can take this entire prompt and when we're done with it, we can simply erase it. The prompt never gets stored anywhere else. We take that prompt, we add the context about our business, we get our generation back, we mask all of the PII data and then we delete the prompt so it never enters into the LLM in a stored way. None of that sensitive data is learned by the LLM Complete power. Now to show you exactly how this works, to kind of dig into the technical details and the architecture of how our team, many of them over there, put this all together, we're going to bring back up Srini. Srini.

Thank you, Patrick. So let me explain the architecture, the trusted AI cloud architecture. It's grounded in what Mark was explaining about trust. So that your data, the two things you had to all remember, we never look at your data salesforce. Also, we never share your data with any other customers. We don't mingle it and any learning happens. For your data is within your trusted boundary for your company. Those are high level concepts. But the way our architecture builds up is at the bottom layer. Let me explain. At the bottom most layer is what we call our trusted infrastructure layer, Hyperforce. Hyperforce gives us data residency, compliance, security. It's also net zero. On top of that we have a data cloud layer. As Mark explained and Patrick explained, these are lakehouse petabyte scale real time data. Lakehouse architecture built natively into the platform. So you get to use the full power of the platform. And data cloud allows you to do a unified profile, it allows you to do zero ETL copies. So if you already have your own etl, it allows you to reach out onto that. And then it has lot of connectors and using our mulesoft connectors and it has a lot of governance and using our MuleSoft connectors you can bring in billions of records and petabytes of data and leverage all of that in the rest of the CRM. And the next layer is what we are calling open models. There are a lot of models. Every day you see the new model coming in. What we believe is where this is going to go is there'll be multiple models and some models will be good. For some tasks you don't need a heavy duty model for every task. At some point, based on the sick pricing and performance and what it is there, we'll pick the model, but we don't want you to figure it out. Each model has to be optimized for security, for compliance, for bias, for everything. We'll do it for you, we will abstract it for you, we'll run a model tournament and give you the best use cases and we'll solve it for you. And that's why an open model is important. So we'll have lot of salesforce models which I'll go a little bit deeper into it. We will let you bring your own models. A lot of our customers have big data science and AI teams and they want to build their own models. We'll let you bring your own models or we are going to use any of the partner models with lot of these top of the line models. And that's what the open model ecosystem is there. The next layer is ultimately anytime you call a model, we want the trust layer, the trust GPT layer. This layer is super important. This allows us to securely get data either from our data cloud or your customer data. You have to do dynamic grounding, which is what Patrick explained, and then you need to be required to toxicity detection or data masking and auditing. It's very important. People want to know what all things happen and people want to know what is my audit trail of all the prompts you're doing. And then you want to ensure that none of it is retained in the models. That's the layer. Now let's say this infrastructure is there. What does it give you? If you are our applications, all our applications will come up with an assistant. So if you're a sales cloud, you'll get a sales cloud assistant which will help you close deals faster. If you're a service cloud agent, you'll have an assistant which will help you close cases faster. And as Mark said, if you're Slack, Slack, I think personally that is going to be the UI for AI. It's that entire interface. Slack is going to wake up and allow you to. Slack is where the enterprise knowledge is there. It will allow you to do that. That's what if you're users, that's great. But Salesforce always empowered a trailblazer. What if you're a trailblazer? Now all our trailblazers can build builders. Whether you're a low code or a pro code trailblazer, we'll have prompt builders. You see how important is prompting. So you will be able to build rate using our app builder tools, you'll be able to build new generative AI apps suddenly. Now if you are an ISV on our platform, you get the entire stack and you can build a whole new class of generative apps and put it on the app exchange. If you are an SI partner, you can use this stack to implement and generate more value for our customers. That's how the entire thing is going to come together. And to go a little bit deeper into how the GPT layer works and how the data flows work, let me explain a little bit. In the CRM apps from the apps you're doing a prompt and the prompt is going to be combined with your company data in a secure data retrieval. We do the dynamic grounding do any data masking is required and it goes through a secure gateway in the gateway allows you to talk to any of the models. There'll be Salesforce hosted models in our own VPCs or you may want to call an external model with the shared trust boundary. And the critical piece is we'll ensure that none of the data is retained there. No prompts, nothing is retained there, no context is retained in the the LLM that is what we call zero retention. Once it generates, we still want to have toxicity built filters, bias filters and things like that. And obviously for a lot of CSOs and enterprise data architects they'll want audit trails and that's what goes back to the CRM app. That's how the GPT trust layer works. Let me explain a little bit why I think right LLM for the right task is important. So we'll have either we'll have great models, best in class model for specific tasks OpenAI where the data will Be still retained in Salesforce but with joint moderation Salesforce will host in our infrastructure globally say in our VPCs multi tenant models on our side or we'll allow you to bring in and at the app layer in the gateway you will not need to know our promise to you. History historically has always been that we abstract all the complexity as things come in. We'll run a model tournament. Model A may be very good today. Model B will come and that will be good for this. We will abstract all of that. We will do the run the model tournament. We'll pick the best and cheapest as things go and keep on changing as this space keeps evolving. We'll protect you, we'll future proof you. That's our promise to you. I also want to talk a little bit about our deep investment in Salesforce LLMs. I hinted at the start but if you see we've been investing in analytics right from 2018. We have a world class AI research team and we published more than 200 papers. Research papers, these are all peer reviewed journals, very hard to get into. More than 200 patents in this area and some of these things models we were the starters, we are the pioneers and some of the models we have we are what we call SOTA or state of the art models. We published and we'll continue to invest in those models. Use open source models, use the partner models in an open LLC system. Pick the model which is right for you in the cheapest best way to do the job and handle all this complexity for you so you can do what you do best which is providing value to your customers. And with that I would like to thank all our AI researchers and engineers and some of them in the room. Can you please stand up and get a round of applause. Thank you. Next, what we want to do is we want to show you how AI Cloud delivers trusted customer success for F1. With that, please roll the film.

In the past, the Formula one experience could have been seen more romantic. With a picnic on the hill of A and the heroes. The evolution from the old days has been incredible. Today the F1 experience is very special.

F1 is the greatest sporting and entertainment spectacle on the planet. The smell of the tires, the sound of the car roaring past.

As soon as the lights go out, you know you are watching something history may never see again. Try to survive has been phenomenal. To capture new fans interested in the behind the scene experience. Normally now we're driving it's exciting fans.

Worldwide we have over 500 million fans, over a third of them are new in the last four years. Formula one keeps getting bigger. The impact keeps getting bigger.

Our database is continuing to grow 30.

40% year on year. The key for us is to make sure that we have the customer at.

The center and therefore our strategic partnership with Salesforce is imperative. We want to make sure that we're designing an experience for our fan like no other. We have 23 races, but only 1% of our fan base actually gets to be at a race.

And so how do we engage the other 99%?

Fans have changed. We've seen a much younger and a much more female fan base.

They'll have their TV broadcast in front of them.

They'll have a device showing all of.

The live content, another showing all of.

That rich data that comes through.

Everyone has a different relationship with Formula One, with Customer360 and that single source of truth, we're creating these digital experiences that really resonate with a fan at an individual level.

With AI data and CRM, we are now able to innovate like never before. Innovation has been always the drive of our success. The DNA of Formula one from an AI perspective, we can truly talk to.

Our fans on a personalized one to one basis.

Speak different language, have different narratives, understand.

Those different markets, be proactive, create these magical connections and bring them closer to the action than ever before. The key to doing that is data.

We've been collecting data across all of these touch points. We have so much data coming into us.

Salesforce helps us visualize the data in a faster way and we're using data cloud to create personalized experiences in a way that nobody's ever seen before. We like to think ourselves as a fan company and as we grow our fan base and we grow our data, working with Salesforce, we're finding loads of new ways to bring those experiences to life.

That's really what it's all about. The joy of our fans embracing the unique experience in Formula one. This is really magic for us.

And to bring it to life. Please welcome Sanjana Perulaker, Senior Director, Product Marketing.

Hi, everyone. Thank you, Srini. So you've seen how generative AI works, so now we're going to have a little fun and show you how you can experience it day to day and get started. So before we get into it, I want to give a hand over here for Tim and Dylan, who are our demo drivers. All Right, now, Formula 1 is using AI data and CRM seamlessly behind the scenes to provide amazing customer experiences. And as Mark mentioned, we're here to talk about AI today. But it all starts with the data. And what you're seeing here is the homepage for Formula One. It has all of their rich fan engagement data, every touch point, every activation and every event that the customer has been to. But how did we get to this neat, unique, connected view? Well, it all starts with connecting to all of your data. And Data Cloud makes it super simple to connect to all of the data that is relevant to your ultimate customer experience. In this case, it's the fan for Formula One. So you can connect to any customer cloud, any external data source or data lake, or even any Legacy system using MuleSoft. And as we know, data is the fuel for AI. So being able to bring in all of this data, no matter where it sits, is extremely important for providing that end personalization. So once we've connected to all of that data, we need to make sure that it's harmonized into one consistent format, because you have data sitting in a lot of different systems. So a single fan could be represented in an IP address, in maybe a touch point with your mobile app, or maybe their Hotmail address from their college account or something like that. But they're one customer with one set of needs. So with Data Cloud, you can harmonize all of that data into one consistent view. And with that, the data is now ready for any sort of personalization that we want to start with. And the end result is actually this very clean, very unified view of the end customer. I can now see every single touch point that Abby has had with Formula one. The races she's attended, what's coming next, the purchases she's made, really anything about her that gives me that full picture of who she is. So now that I have this unified view of the customer, this is where it gets interesting with AI, starting with marketing. Now, what we want to do is create a landing page for our upcoming event. And I can tell you, as a former Salesforce admin, myself, and a current marketer, creating landing pages is hard. And it's hard for two reasons. The first is it's difficult to personalize it just the way you want. And the second is that you never know how it's really going to perform. Now, this is where Einstein GPT is extremely important in helping to do this quickly. So as I start to prompt Einstein GPT with what I want to do, in this case, creating a landing page, all of this content that's being populated on the page is not just content coming out of nowhere. This is content based on what has performed best in the past. So I can rest assure as that marketer that I'm not only building quickly, but I'm building for that end personalization that my customer is really going to want. So Einstein GPT is going to help me create more personalization on this page, more product recommendations, and I even want to add an interactive map for our customers and fans that will be on site at the Miami Grand Prix. Now, as a marketer, this is where my job ends and a developer's work begins. So let's head over into the developer environment. Now, right here within the ide, my developer can customize this map and make it super interactive for that end landing page, which is killer for our F1 fans. And with just a few prompts and descriptions of what I want to achieve, the code will be automatically generated. And this is powered by our own Salesforce large language models. Now, I want to pause here for a second because in this new era of generative AI, there's a lot of talk about the future of work and what these new roles will be of the future. And the future is our trailblazers. Our trailblazers are building with Apex today. And with these tools, they'll be more efficient than they have ever been before, which is simply incredible. So now that we've built the page, let's see what it looks like. Now, with this page, this is fully personalized to the end fan, personalized to them and their journey at the Miami Grand Prix. Okay, so another really special part about F1's business is the way that they treat their end fans with their hospitality reps. So for them, a sales rep is a hospitality rep and they pride themselves on this trusted relationship. Now, this trust can take a lifetime to build and a moment to break with the wrong level of personalization or feeling like that rep really doesn't know them. So Einstein GPT helps them see scale this relationship and nurture it with the most up to date information. So the first thing I want Einstein GPT's help with here is to update my account description and it can help me do this really quickly, bringing in that public data that's relevant to my customer alongside private data, which is really important. It'll also surface my relevant contacts that I might want to reach out to for the upcoming Miami Grand Prix. So it's not just surfacing knowledge, but it's surfacing actions which helps me go faster as that hospitality rep. So next I want to compose an email and invite my end customer to an upcoming race with a really tailored email here. Now another special thing that I want to call out about Einstein GPT is that this isn't just based on everything a rep has done in the past. This is based on outcomes. So this email that's being generated is based on the emails that have been most highly performant with customers in the past. So we're going to go ahead and edit that email, bring it into the email composer, and send that right to the customer. So the next step of this story is super important because this is where it gets very personal with our customer. Inside of Slack. Now, the hospitality reps within F1 are using Slack as their centerpiece for productivity across all of their channels and all of their customers. Now, this is the updated sales homepage for all the Formula one hospitality reps, and it's enriched with all of the amazing data they have in CRM about their customer. So I can see over here that this end customer that I just sent the email to has accepted our invitation to the race. And what's automatically been created is this Slack canvas, which has really the best starting point I could ever ask for in terms of information that I want to give to my customer. It has some relevant information for VIPs, some experiences on site, contacts. But as that hospitality rep, I might want to personalize this a little bit more. And Einstein GPT is there for me as well. I can ask Einstein to help me identify what the best experiences are at the Miami Grand Prix and then update this canvas. So I've gotten that great first stop with Generative AI and I can also continue to customize this. So we're going to share this with the customer in our joint channel, because we're collaborating directly with the customer here, and we want to make it really, really important for them to have every single experience they want. And it looks like they actually have some questions for us. And one thing that I really love about Slack myself at Salesforce is using a huddle for those quick questions that might not require a whole phone call or a whole conversation. So we get on a huddle with the customer and we understand a little bit more deeply what they want to experience on site at the Formula one race. Now, not only was I able to do that super quickly, but after the call is done, I'll get a helpful summary right in the channel that I can use later. So this is just a snapshot of all of the various ways that Formula one can be using AI cloud across their entire business. Back to you, Mark.

Very good. Thank you very much. Well, I think we're beginning to see the integration between what's going on with Salesforce, with data and with AI. And as our customers are starting to bring that together, at the heart of it, it's going to be trust. You could see in the example where certainly there's a role for public information, where that public information is not coming out of our database, but we want to augment our capability. But then as we begin to augment our own capabilities inside our app, we want to be able to operate at the trust layer. So to really help us to understand that at the next level, please welcome Clara Shih, who's the CEO of Salesforce AI. Clara.

Thank you, Mark. And thanks to all of you. I am thrilled to be here in my new role and to talk to you about what we're doing in Generative AI not a year from now, not five years from now, but today, this month, what we're shipping. It's really amazing just to think that a year ago, most of the world didn't know what a large language model was, and yet here we are today, all in together with the trust and the security that you would expect from Salesforce. We are bringing Generative AI to every application, to Salesforce, to every plat, to our platform and to our entire ecosystem. And we're doing it in a trusted way, in a way that's rooted in the job to be done. Highly contextual, highly relevant, highly secure. And the best part is, from the very beginning, our platform was architected for pro code developers, for low code and for no code. Which means that if you have a big data science team and machine learning engineers and you want to train and tune your own model, we have a solution. But there's also a lot of companies that don't have a data science team and don't have a lot of machine learning engineers. And those companies too can take advantage of all of these capabilities. We're really democratizing Generative AI. Now, AI Cloud is powered by Einstein GPT. And Einstein GPT can write code for you, it can generate flows for you with Flow GPT. It can write sales emails, generate service responses, marketing landing pages, a lot of what we've already heard about and seen so far. Now, the amazing thing is, if you think about the example that Patrick showed earlier of the sales email, that is something that a salesperson in your organization could do manually. But to get that quality, it'd probably be one of your top salespeople only. And it might take that person several hours or even several days to collect all of the context about that customer, their marketing interactions, their service support issues. That are open, what they're doing with their commerce, what they have in their commerce basket to be able to craft that perfect email. And with Einstein GPT we do that in an instant. And we also do it for every seller in your organization, every service agent in your organization, every marketing manager in organization, every commerce manager, every developer, not just the top few percent. That is the power of Einstein GPT. Now in March we unveiled Einstein GPT as the world's most trusted AI for CRM. And since then we've been very busy. Just last week at Connections we launched Commerce GPT and Marketing GPT. Last month we launched Slack GPT at our New York World tour we were here and before that Tableau GPT and Tableau Pulse at our event in Las Vegas. Well, today we're adding to the mix Sales GPT and Service GPT bringing generative AI into the flow of work where knowledge workers are already spending time and doing it in a way that is harmonized across the customer.360 because of course for us to succeed with SalesGPT we need the context from marketing and from service and from commerce and so on. And as we started developing these products with forward thinking trailblazers like Basili and F1 and Shire from AAA, we'll hear from in just a moment we asked ourselves what is the most valuable, highest impact place for us to start with generative AI? What are the most important operational challenges and bottlenecks and opportunities to drive consumption, consistency and better performance across each of these departments? And as we worked together we realized that it was focusing on those areas where service agents struggle, salespeople struggle. I'll give you some examples. In service, when there's a customer waiting to hear back from you on the phone, when they're waiting to hear back your response in a chat, it's very stressful for the service agent to have to look up all of these product documentation and looking through all these knowledge articles to find that right answer for that customer. We can automate that. The service agent can focus on connecting with the customer to augmenting his or her potential and really becoming more than a service agent. Same thing in sales. Instead of the seller trying to spend all their time piecing together that perfect email to get a prospect to meet with them. We automate that so that sellers can get out there and meet with their customers, really connect and drive value and so on and so forth. AI cloud drives productivity, drives revenue for every workflow, every user, every department, across every industry, every segment and around the world. And to hear more about that, I think we're going to here from aaa. Sharey, thank you for being a trailblazer. Shere Avedi is the EVP COO and CTO of aaa, a fantastic organization here in North America. Trey, thank you so much for being a longtime Salesforce customer and for joining us.

Thank you. It's really nice to be here. So I am actually from aaa, the Auto Club group, the second largest club in North America.

Well, thank you for being a longtime Einstein customer. On the predictive AI side. Now, as we embark on generative AI, how are you thinking about where to start?

So the way we're thinking about it, honestly, when all of this hype started, the first thing that happened is my CESO rushing into my office spooked, right? The look of spook in his eyes saying we have to shut everything down, you have to shut everything down. And I said, well wait a minute, slow down a bit, right? I get it. This thing is pretty scary in terms of a tool like this in the hands of just about anybody. But at the same time we've got to be able to look at this and see how can we leverage it for our benefit to. But sticking to what's core to AAA was safety and trust in mind. So I didn't want to stifle all of the creativity of our people because I had people from underwriting, from all kinds of departments coming up with 150 different great ideas, right, that they could do with ChatGPT. So that was the first thing. But the areas we're looking at is three different areas. One is customer service, just like you mentioned, just to be able to give our agents more time to actually listen and hear the customer versus searching for things. Another is in the middle for support processing. I can reduce all of that processing time, bring down the cost, make it more effective and quality. The third layer is really for my developers within DevOps having to identify, troubleshoot and also increase our testing capability, it's just incredible.

So forward leaning in all of these areas. So you mentioned earlier hype and there is a lot of hype when it comes to AI. What advice would you have for everyone in this room and who's listening on streaming, on how to cut through the hype and really focus on results.

So again from, from my perspective, to calm my CISO down, I said look, I get this, but at the same time you've got just about anybody knocking down my door right now saying we've got the best model, we've got this AI capability or another. What our approach is Partnering with partners that we have in our collection, such as Salesforce and other partners. Because what we want to do is we don't believe, I don't believe it's the right thing to take another layer and component, but rather fold it into the fabric of our platforms and that's really how we're moving forward. You want to be able to partner with folks that have the wherewithal, right. That can go shoulder to shoulder with you. And that's really what I'm looking for. Not the, you know, the quickest spin off or someone out there that has the greatest thing, but then leaves my back door open because that is hugely important for a company like us.

Well, thank you for your shared value of your trust, your collaboration for your business.

Thank you.

Now, one of the most powerful things about AI is that it learns. It learns over time, it continuously improves or if it doesn't have the right feedback, it could get worse over time. Thinking about model and how it gets better. And this is really where us being the number one AICRM really makes a huge difference because there's two aspects of this learning. It's called reinforcement learning. So the top section is a term that probably many of you have heard in the industry called reinforcement learning from human feedback. When a sales email is generated, when a service agent response is suggested, does the worker use it? Do they edit it? Do they make significant changes? How does that differ across other members of the team? So us having all of those workers in Salesforce day in and day out allows us to figure out what is the reinforcement learning from human feedback. What is effective? How do we use that to tailor and improve the next set of suggestions that we give to salespeople and service agents? It's a big part of reinforcement learning. Well, there's another part of reinforcement learning that's very important too, and that's reinforcement learning based on objective business outcomes. Because in addition to being a place where Salesforce where we store your customer data, we also store your customer outcomes. And so we start to ask ourselves and create data pipelines around, did that sales email actually cause the sales opportunity to advance a stage and ultimately did it become closed won? Did that marketing landing page? Did that commerce Product description Did that concierge experience cause the customer to convert and buy the items in their shopping cart? These customer outcomes, this reinforcement learning from human feedback and from business outcomes will ensure that every customer using AI cloud will develop the best models, not just today, but over time for every use case specific to their industry, specific to their task, specific to Their department. And it's a really exciting area that our research team, our engineering teams have spent a lot of time on. And so with that, let's bring this all to life with another customer story. We heard from AAA just now. We heard from, from Gucci. Let's hear from Rossignol. Anyone who skis or likes alpine sports has heard of this amazing company based in France. And let's hear from their CEO Real trailblazer, Vincent Waters and his team on how they're using AI, cloud and the combination of AI plus data plus CRM to transform.

What I love the most about the mountains is the energy that I get every time I go up. You get a feeling of excitement, of discovery. Every step you take opens up a new horizon, and I think that is beautiful. We are very clear on our greater purpose and our values. We want to inspire people to spend more time in the mountains. We want to carve movements of sustainability and human potential. There's always a very strong emotional connection with our brand. Nostalgia, anticipation, excitement. But if you want to create and foster loyalty with our consumer, we need.

To personalize every interaction.

Salesforce is a very precious power to make that happen. This is a unique turning point for Rossignol. The seasons are getting shorter and it's going to become more challenging.

We are at the crossroads.

We are progressing to a new phase.

Where we need to deploy the full.

Emotional connection with our consumers across four seasons. This brand is really the oldest, most recognized ski brand in the world.

Eurocinia represents the beating heart of the mountain.

We make skis for beginners. We are making skis for the highest level athletes. When you have such a wide range, it is a curse and a blessing. How do we reach our consumer with the product that they actually need in that moment, Customer360 is really about understanding the desires and needs of our audiences and how racial can deliver a better experience. When that is clear, we express ourselves with more authenticity. And through authenticity, we create an inspiration. The world is changing fast and now we have almost an infinite amount of data coming from all the touch points.

Salesforce help us collect those data, analyze.

Them, create efficiency, create personalization. And now with AI, with Salesforce, we are able to really engage with our consumers seamlessly across seasons. We can frame it, we can give it guidance. They're going to get proposed the right product that inspires them. Our CRM, our data together with AI is going to create a circle, a virtual circle that is going to elevate the consumer experience. The partnership with Salesforce started 12 years ago. And now we have an exciting new chapter. This is going to be a new era for skiers and snowboarders and bikers around the world. I believe that we can be agile, facing new challenges.

Salesforce will help us to adapt and strive in an ever evolving world.

Incredible story. Sanjna, please help us bring it to life by showing us how Rossignol uses AI Cloud.

All right, thank you, Clara. Back for another amazing demo of how Rossignol is using AI Cloud to deliver these incredible experiences. Now, Rossignol cares deeply about their brand voice and tone and bringing that to every personalized recommendation they bring to their customers. And they're thinking about launching this new Heretic bike, which they know will be a hit for their customers. So it all starts within Commerce GPT. Now, this helpful guidance center here within Next Best Action is going to help me with that checklist of what I need to launch a new product in market. And it looks like we're actually missing a few things here. We're missing some product descriptions and just like I said in the previous demo, creating these descriptions or creating a marketing email, a sales email, this takes a lot of time and it's no different here. But Einstein GPT is here to help by automatically generating product descriptions that are not just descriptive of the product, but that are actually in the brand voice and tone that Rossignol wants to bring to their end customer. Now, there's a catch here. Rossignol is also a global company, so it's not enough to have these product descriptions generated. They also need to be localized for the languages that are most relevant to their customer base. In this case, we want to start with Spanish so we can translate all of these descriptions with Einstein GPT in the click of a button. Now, normally this would take a lot of time creating the descriptions, getting them to the local teams, getting them translated, and then taking it that extra mile of inserting the voice and tone of that language into the description. It sounds painful because it is. But within a few clicks, Einstein GPT can help them bring this bike to market with this commerce experience. Now, once this bike is launched and it's out there, it's flying off the shelf. People are so into this new bike and all of our brand managers globally want to make sure that they have the right understanding of how their KPIs are being affected by all of these net new sales. Now, these powerful visualizations you see here would normally take a lot of data analysts, a lot of work behind the scenes to create every time you wanted to refactor some of the elements of the dashboard. But with the new Tableau Pulse, this is available for business users to keep track of their business as it happens. It's keeping up with the speed of how things change, all in a trusted manner. And even if this dashboard isn't enough, that business user doesn't need to go back to the data analyst for changes. Einstein GPT has your back here as well. You can actually just ask a question in natural language here and einsteingpt will generate this additive visualization here with a description answering your question so you can get on with your day. No need to meet with those data analysts to generate this and wait a long time for those insights. Now another element that Rossignol cares deeply about is the way that they service their end customer. And there are two key reasons here. One, they want to make sure that their service time, their case resolution time is extremely short for every case and two, they want to make sure they have happier customers. So this is where Einstein GPT within service is so important. As this company question is coming in through the service terminal. Einstein is providing these helpful recommendations for me as I go along as a service rep. Now, the service manager is always in the driver's seat. They can accept, edit or decline those recommendations. There's always a human in the loop. But just the sheer fact that these recommendations are in my place of work as a service manager is a huge time saver. It's keeping me from chair swiveling between different knowledge articles, searching through my whole knowledge base for that perfect answer for the customer. As the conversation goes on, I even have next best action giving me those relevant personalized product recommendations for that end customer. And the cherry on the top here actually is that as this conversation is going on, once I close the case, I can easily summarize it and share it with the rest of the service rep. So for example, if our field service reps are out there in the field, they're not behind a laptop. They can access this best practice in the form of a knowledge article that is shared amongst the whole team, which is amazing. Now I hope between these two stories, these simple stories, it's got your imagination thinking a little bit on how you can get started with AI cloud today. The possibilities are really endless when you bring AI data and CRM together. Thank you.

Thank you, Sanjna. So, so many of you have been asking how you can get started with AI cloud. Well, what did we do? We talked to our customers and we sat down with Gucci and AAA and Rossignol and the Royal bank of Canada, so many others. And we realized that the world leading best in class AI first companies were using a number of solutions together. First CRM, having AI in the flow of work, where salespeople, where service agents work, marketing managers, commerce managers, developers, business analysts, where they are both for the reinforcement learning and also so that we can automate the prompt engineering for their specific job to be done for their industry, for that specific customer and their needs. Then we saw that the best in class customers are using Slack because it's all about collaboration. Once we have those generated outputs, it's not about individuals, it's about teammates, coworkers coming together and saying, how do we take what the AI has produced and make the landing page better? How do we make the commerce description better? Let's work together across teams. How do we use generative AI to summarize all of these conversations, all this knowledge that's sitting inside of Slack that represents our organization's brain. Then we see these organizations using Tableau and saying, well, if I want to dig deeper and really understand the data that's being used to train and fine tune and ground my prompts, how do I explore that data? How do I have a conversation with that data through natural language and of course Einstein. We've been talking about both the predictive aspects of Einstein as well as now the generative aspect of Einstein. And then very important is the data. How do we get that data from across the organization, Both data that's in the cloud as well as data that is on premise and use Mulesoft to bring that together and use data cloud to unify the data, to harmonize the data, to create data pipelines that can be used in any model to really drive results. But it's not just the technology, it's also a tremendous change management exercise. And that's where Salesforce Services comes in. Being able to partner with you to skill up your organizations. And I'll talk about what we're doing with our trailblazers in just a moment, but everything that you need to get to a proof of technology to make sure that it complies with your company governance, with industry regulation, and then to go from there to coming up with a strategic plan. Which use cases are you going to start? Will it be similar to what Charay is doing? Maybe it's different for your organization. We want to help you figure that out. And so Salesforce Services is here to help as well as amazing AI first partners like Julie and her team at Accenture, our partner at Deloitte PwC and more coming soon. So we're excited to help you get started. And as I mentioned earlier, all of this is shipping. Now, our engineering and product and research teams have been busy at work. They've been burning the midnight oil to make this happen. This is going to be the summer of AI. It's gonna be even better than the summer of love. It's summer of AI and we're gonna have sales email generation. We're already piloting that with a number of you. We're gonna be shipping service reply recommendations to augment your service agents, just like Vasily's team at Gucci. We're gonna be shipping summarization. You just saw that demo from Sanjna, so on and so forth, every cloud busy at work. We've mobilized the entire team, the entire company around AI and helping ship that and get that into the hands of our customers. And of course, any change, management starts with our people. It starts with your employees. Starts with our employees. And so we're also thrilled to be launching today a new series of courses through Trailhead. It's our free online learning platform that any of you, any of your employees can start signing up for today to start earning. We have over 35 AI badges that will be available. Everything from the very basics of what is generative AI. How do you approach AI in a responsible, ethical way that honors data security and data privacy, all the way to the most advanced courses around training and tuning and prompt grounding. Some of the more advanced concepts of that we talked about really runs the gamut. So please check this out. This is going to take all of us and all of our employees to reskill and learn these new capabilities. So with that, I will hand it back over to Mark.

Very good. Please give Claire a pin. Thank you. Well, I think that from our perspective, it's all about trust and certainly it's all about responsibility going forward. And I think as we start to see the potential of what AI is starting, like you saw in the first demo, where the AI was actually writing its own code, I think that when I was talking with Sam over that dinner and I said to him, I think we're going to have to rewrite each one of our applications to take full advantage of all these capabilities, he turned to me and said, no, no, you're not going to have to take any time to rewrite those applications. Don't worry about anything. The computer's going to do it for you. And it's a subtle comment about how he sees the future of AI, which is that the computers are more powerful than ever before. Generative AI gives us another generation of capability and we're also going to have these LLMs which do technology that we never thought was possible. So for a lot of our customers, this is about getting going for the very first time with AI. They might have been using predictive AI, they might have been using machine intelligence, they might have been using machine learning, they may have even been using deep learning to understand what was possible with these neural networks. Now they're really exploring this next generation of AI to understand how they're going to take the productivity of their organizations to a new level. I thought it'd be a great opportunity now to introduce you to Julie Sweet, who's the CEO of Accenture. Julie, thank you so much for being here. We're so happy to have you. Please give her a round of applause.

You.

Hello.

Julie. Can you give us your vision of what you're seeing with your customers? You're working with the largest customers all over the world and helping them to deploy these amazing new next generation enterprise technologies. What are you seeing as the potential for AI with them?

Well, thanks, Mark. And first of all, it's great to be here. It's great to see all the things we've been talking about really come to life. And one of the things that is top of mind with a all of our clients is this point around trust and security. And I would emphasize this technology is really early. And one of the big advantages that I think you have by working with Salesforce is that trust and security are being built in from the beginning. And you're bringing together that with the deep expertise around the use cases. And, and that is really resonating with our clients who want to make sure that they don't get ahead of themselves before some of these things have been worked out. And yet at the same time, they believe, as we believe, that generative AI will transform how they work, how they engage with clients. And so I have a very simple thing when we talk to clients is that, you know, and they're saying, how do I get started? And so with Salesforce, one of the first things I say is, well, first make sure you're using all the AI that you've already paid for.

Right?

Because it's incredibly powerful. Accenture is a user, 55,000 users. We use the predictive AI, we use visualization. We have insights in the process, hugely powerful. And lots of our Salesforce clients are not fully using it. So make sure you're doing that. Then it's all about data, which is why the data cloud is so important, and then from there, make sure that you're using business criteria to evaluate the use cases. And that's where the work we're doing is so important in the AI accelerator hub that we just announced with Salesforce, because that's about bringing the power of Salesforce and Accenture and all of our industry knowledge, our knowledge about customers and marketing and sales and service with you, the clients, together to say, how do we create the absolute most powerful use cases that have a return? And I know that Accenture's thinking about this and Salesforce thinking about this. We know that our clients need a return fast. And so we're trying to really focus on how do you take the technology, the maturity it's at today, bring it together with all that Salesforce has, and really deliver value early to our clients.

Julie, I want to ask you, you know, you're the CEO of one of the most successful companies in the world, one of the largest companies in the world, and you're probably thinking about productivity in your own organization. You have so many engineers, you have so many sales and service marketing professionals. How are you seeing the future of Accenture? What do you look at for your own productivity increases?

Sure. Well, we think specifically about AI in three ways. How do we help our clients, how do we use it to deliver for our clients, and then how do we use it to operate Accenture? And we've been using diagnostic AI, which tells you what happens in predictive AI for years, years across our business and particularly in our sales function with Salesforce. And I will tell you that when I became CEO in 2019, we had not done a great job of making sure all of our 55,000 users were using the predictive capabilities. And we have a phenomenal sales officer that I appointed who was deep in Salesforce, who very smartly made sure that we were using that. And when the pandemic came, the ability to have predictions. And we have proven now for three years that bottom up forecasting is never as accurate as what we do within Salesforce. And so now the idea of being able to take the power of all of that, which is already a part of our process, and add on it generative AI to draw more connections, be able to get to insights faster, we think is hugely exciting. But what I would say to you is that just like we had to change our process and train our people on how to use the predictive AI, this is just technology until it becomes embedded in how you work and change management to make sure people understand it and the skilling that we were just talking about. And I think that's where our partnership is so important because it makes sure that the technology actually delivers value by coming in with a point of view on how do you change, how do you get people to adopt, how do you rewire your processes and very importantly, how do you get your data.

Julie, I want to ask you about trust and if I could, in the last question here, which is this idea that, you know, we all read this story about this lawyer who submitted these briefs built on ChatGPT asking it if it was correct and it wasn't. We've all seen our own personal examples where we're trying to, you know, ask it, maybe asking, asking some generative AI system, maybe it could be inflection or maybe it could be one of these other systems to help us to understand something about our business, but then wondering, where is all this data going? Or we might be asking ourselves how should we be looking at the fundamental security of our own company's data? I'm sure you're thinking a lot about that. I know you're getting a lot of questions from your customers. Where do you see this kind of relationship between trust and generative AI that we've talked about today?

Well, this is a topic that I'm really passionate about. We both are on the business roundtable and actually a good over two years ago, I led the the work on responsible AI that was all about making sure that you could be clear about what responsible AI was and how to implement it. And in fact, at Accenture, we have a compliance program that's overseen by our audit committee of our board that's just like every other compliance program. And I would say one very practical piece of advice, if when you leave this room, you are not able to pick up the phone, call someone in your company and have them tell you where is AI being used? What are the risks, how are they being mitigated, how are they monitored and who's accountable? You do not yet have responsible AI. And I will tell you that when we go into companies and help them set up these programs, we have pre populated views. And when we get to Salesforce, we say, here's all the AI that you have in your organization and here's the low risk, right? Because it's built in. And that is really important as you move along this journey of generative AI understanding every time you're using it, where is the risk? And make sure that you're partnering with companies like Salesforce where you can understand that it's either low risk or you absolutely are clear on what the risks are, how to mitigate them and how to monitor it.

Well, thank you, Julie, so much for coming today. We're really thrilled to have a great partnership with Accenture. You've done so much amazing work with not only our company but so many of our customers. We're thrilled to have you. We're just starting out on the future of our journey of AI. We have been looking at so many companies to invest in. We've done some of that work jointly as well. We just doubled our AI fund and we're looking forward to getting our customers to start their AI journey. Hopefully they'll start them with Salesforce and Accenture. Thank you for coming today. Thanks to all of you as well for being with us. Thank you. We're so grateful for you and we have some terrific demonstrations of the technology outside for you as well. And welcome to Salesforce's AI Day. Thank you so much.